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Journal articles

Published Research from the user community and EUMETSAT

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Access the extensive bibliography related to EUMETSAT climate monitoring activities

Last Updated

04, November 2020

 

2020

Blunden, J., Arndt, D.S. (Eds.), 2020. State of the Climate in 2019. Bull. Am. Meteorol. Soc. 101, S1–S429. https://doi.org/10.1175/2020BAMSStateoftheClimate.1

Healy, S.B., Polichtchouk, I., Horányi, A., 2020. Monthly and zonally averaged zonal wind information in the equatorial stratosphere provided by GNSS radio occultation. Q. J. R. Meteorol. Soc. qj.3870. https://doi.org/10.1002/qj.3870

Lang, T., Buehler, S.A., Burgdorf, M., Hans, I., John, V.O., 2020. A new climate data record of upper-tropospheric humidity from microwave observations. Sci. Data 7, 218. https://doi.org/10.1038/s41597-020-0560-1

Safieddine, S., Parracho, A.C., George, M., Aires, F., Pellet, V., Clarisse, L., Whitburn, S., Lezeaux, O., Thépaut, J.-N., Hersbach, H., Radnoti, G., Goettsche, F., Martin, M., Doutriaux-Boucher, M., Coppens, D., August, T., Zhou, D.K., Clerbaux, C., 2020. Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI. Remote Sens. 12, 2777. https://doi.org/10.3390/rs12172777

Steiner, A.K., Ladstädter, F., Randel, W.J., Maycock, A.C., Fu, Q., Claud, C., Gleisner, H., Haimberger, L., Ho, S.-P., Keckhut, P., Leblanc, T., Mears, C., Polvani, L.M., Santer, B.D., Schmidt, T., Sofieva, V., Wing, R., Zou, C.-Z., 2020. Observed Temperature Changes in the Troposphere and Stratosphere from 1979 to 2018. J. Clim. 33, 8165–8194. https://doi.org/10.1175/JCLI-D-19-0998.1

Tian, X., Zou, X., 2020. Comparison of Advanced Technology Microwave Sounder Biases Estimated Using Radio Occultation and Hurricane Florence (2018) Captured by NOAA-20 and S-NPP. Adv. Atmospheric Sci. 37, 269–277. https://doi.org/10.1007/s00376-019-9119-5

Trindade, A., Portabella, M., Stoffelen, A., Lin, W., Verhoef, A., 2020. ERAstar: A High-Resolution Ocean Forcing Product. IEEE Trans. Geosci. Remote Sens. 58, 1337–1347. https://doi.org/10.1109/TGRS.2019.2946019

Waliser, D., Gleckler, P.J., Ferraro, R., Taylor, K.E., Ames, S., Biard, J., Bosilovich, M.G., Brown, O., Chepfer, H., Cinquini, L., Durack, P.J., Eyring, V., Mathieu, P.-P., Lee, T., Pinnock, S., Potter, G.L., Rixen, M., Saunders, R., Schulz, J., Thépaut, J.-N., Tuma, M., 2020. Observations for Model Intercomparison Project (Obs4MIPs): status for CMIP6. Geosci. Model Dev. 13, 2945–2958. https://doi.org/10.5194/gmd-13-2945-2020

2019

Belmonte Rivas, M., Stoffelen, A., 2019. Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT. Ocean Sci. 15, 831–852. https://doi.org/10.5194/os-15-831-2019

Blunden, J., Arndt, D.S., 2019. State of the Climate in 2018. Bull. Am. Meteorol. Soc. 100, Si-S306. https://doi.org/10.1175/2019BAMSStateoftheClimate.1

Docquier, D., Grist, J.P., Roberts, M.J., Roberts, C.D., Semmler, T., Ponsoni, L., Massonnet, F., Sidorenko, D., Sein, D.V., Iovino, D., Bellucci, A., Fichefet, T., 2019. Impact of model resolution on Arctic sea ice and North Atlantic Ocean heat transport. Clim. Dyn. 53, 4989–5017. https://doi.org/10.1007/s00382-019-04840-y

Gignac, C., Bernier, M., Chokmani, K., 2019. IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area. The Cryosphere 13, 451–468. https://doi.org/10.5194/tc-13-451-2019

Magarreiro, C., Gouveia, C., Barroso, C., Trigo, I., 2019. Modelling of Wine Production Using Land Surface Temperature and FAPAR—The Case of the Douro Wine Region. Remote Sens. 11, 604. https://doi.org/10.3390/rs11060604

Stöckli, R., Bojanowski, J.S., John, V.O., Duguay-Tetzlaff, A., Bourgeois, Q., Schulz, J., Hollmann, R., 2019. Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications. Remote Sens. 11, 1052. https://doi.org/10.3390/rs11091052

Su, C.-H., Eizenberg, N., Steinle, P., Jakob, D., Fox-Hughes, P., White, C.J., Rennie, S., Franklin, C., Dharssi, I., Zhu, H., 2019. BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia. Geosci. Model Dev. 12, 2049–2068. https://doi.org/10.5194/gmd-12-2049-2019

Sun, B., Reale, T., Schroeder, S., Pettey, M., Smith, R., 2019. On the Accuracy of Vaisala RS41 versus RS92 Upper-Air Temperature Observations. J. Atmospheric Ocean. Technol. 36, 635–653. https://doi.org/10.1175/JTECH-D-18-0081.1

von Schuckmann, et al., 2019. Copernicus Marine Service Ocean State Report, Issue 3. J. Oper. Oceanogr. 12, S1–S123. https://doi.org/10.1080/1755876X.2019.1633075

Zhran, M., Mousa, A., Rabah, M., Zeidan, Z., 2019. Utility of GNSS Radio Occultation technique for tropopause height investigation over Egypt. NRIAG J. Astron. Geophys. 8, 45–54. https://doi.org/10.1080/20909977.2019.1617559

2018

Buffat, R., Grassi, S., Raubal, M., 2018. A scalable method for estimating rooftop solar irradiation potential over large regions. Appl. Energy 216, 389–401. https://doi.org/10.1016/j.apenergy.2018.02.008

Buizza, R., et al., 2018. The EU-FP7 ERA-CLIM2 Project Contribution to Advancing Science and Production of Earth System Climate Reanalyses. Bull. Am. Meteorol. Soc. 99, 1003–1014. https://doi.org/10.1175/BAMS-D-17-0199.1

Hartfield, G., Blunden, J., Arndt, D.S., 2018. State of the Climate in 2017. Bull. Am. Meteorol. Soc. 99, Si-S310. https://doi.org/10.1175/2018BAMSStateoftheClimate.1

Massonnet, F., Vancoppenolle, M., Goosse, H., Docquier, D., Fichefet, T., Blanchard-Wrigglesworth, E., 2018. Arctic sea-ice change tied to its mean state through thermodynamic processes. Nat. Clim. Change 8, 599–603. https://doi.org/10.1038/s41558-018-0204-z

Seethala, C., Meirink, J.F., Horváth, Á., Bennartz, R., Roebeling, R., 2018. Evaluating the diurnal cycle of South Atlantic stratocumulus clouds as observed by MSG SEVIRI. Atmospheric Chem. Phys. 18, 13283–13304. https://doi.org/10.5194/acp-18-13283-2018

Urraca, R., Antonanzas, J., Sanz-Garcia, A., Martinez-de-Pison, F.J., 2019. Analysis of Spanish Radiometric Networks with the Novel Bias-Based Quality Control (BQC) Method. Sensors 19, 2483. https://doi.org/10.3390/s19112483

Maranan, M., Fink, A.H., Knippertz, P., Amekudzi, L.K., Atiah, W.A., Stengel, M., 2020. A Process-Based Validation of GPM IMERG and Its Sources Using a Mesoscale Rain Gauge Network in the West African Forest Zone. J. Hydrometeorol. 21, 729–749. https://doi.org/10.1175/JHM-D-19-0257.1

Zampieri, L., Goessling, H.F., Jung, T., 2018. Bright Prospects for Arctic Sea Ice Prediction on Subseasonal Time Scales. Geophys. Res. Lett. 45, 9731–9738. https://doi.org/10.1029/2018GL079394

2017

Blunden, J., Arndt, D.S., 2017. State of the Climate in 2016. Bull. Am. Meteorol. Soc. 98, Si-S280. https://doi.org/10.1175/2017BAMSStateoftheClimate.1

Khaykin, S.M., Funatsu, B.M., Hauchecorne, A., Godin-Beekmann, S., Claud, C., Keckhut, P., Pazmino, A., Gleisner, H., Nielsen, J.K., Syndergaard, S., Lauritsen, K.B., 2017. Postmillennium changes in stratospheric temperature consistently resolved by GPS radio occultation and AMSU observations: Temperature Change from GPS-RO and AMSU. Geophys. Res. Lett. 44, 7510–7518. https://doi.org/10.1002/2017GL074353

Landy, J.C., Ehn, J.K., Babb, D.G., Thériault, N., Barber, D.G., 2017a. Sea ice thickness in the Eastern Canadian Arctic: Hudson Bay Complex & Baffin Bay. Remote Sens. Environ. 200, 281–294. https://doi.org/10.1016/j.rse.2017.08.019

Loew, A., Bell, W., Brocca, L., Bulgin, C.E., Burdanowitz, J., Calbet, X., Donner, R.V., Ghent, D., Gruber, A., Kaminski, T., Kinzel, J., Klepp, C., Lambert, J.-C., Schaepman-Strub, G., Schröder, M., Verhoelst, T., 2017. Validation practices for satellite-based Earth observation data across communities: EO VALIDATION. Rev. Geophys. 55, 779–817. https://doi.org/10.1002/2017RG000562

Marseille, G.-J., Stoffelen, A., 2017. Toward Scatterometer Winds Assimilation in the Mesoscale HARMONIE Model. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 2383–2393. https://doi.org/10.1109/JSTARS.2016.2640339

Stoffelen, A., Aaboe, S., Calvet, J.-C., Cotton, J., De Chiara, G., Saldana, J.F., Mouche, A.A., Portabella, M., Scipal, K., Wagner, W., 2017. Scientific Developments and the EPS-SG Scatterometer. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 2086–2097. https://doi.org/10.1109/JSTARS.2017.2696424

2016

Blunden, J., Arndt, D.S., 2016. State of the Climate in 2015. Bull. Am. Meteorol. Soc. 97, Si-S275. https://doi.org/10.1175/2016BAMSStateoftheClimate.1

Chung, E.-S., Soden, B.J., Huang, X., Shi, L., John, V.O., 2016. An assessment of the consistency between satellite measurements of upper tropospheric water vapor: Upper Tropospheric Water Vapor. J. Geophys. Res. Atmospheres 121, 2874–2887. https://doi.org/10.1002/2015JD024496

Otto, J., Brown, C., Buontempo, C., Doblas-Reyes, F., Jacob, D., Juckes, M., Keup-Thiel, E., Kurnik, B., Schulz, J., Taylor, A., Verhoelst, T., Walton, P., 2016. Uncertainty: Lessons Learned for Climate Services. Bull. Am. Meteorol. Soc. 97, ES265–ES269. https://doi.org/10.1175/BAMS-D-16-0173.1

Yang, W., John, V., Zhao, X., Lu, H., Knapp, K., 2016. Satellite Climate Data Records: Development, Applications, and Societal Benefits. Remote Sens. 8, 331. https://doi.org/10.3390/rs8040331

2015

Blunden, J., Arndt, D.S., 2015. State of the Climate in 2014. Bull. Am. Meteorol. Soc. 96, ES1–ES32. https://doi.org/10.1175/2015BAMSStateoftheClimate.1

2014

Amillo, A., Huld, T., Müller, R., 2014. A New Database of Global and Direct Solar Radiation Using the Eastern Meteosat Satellite, Models and Validation. Remote Sens. 6, 8165–8189. https://doi.org/10.3390/rs6098165

2013

Greuell, W., Meirink, J.F., Wang, P., 2013. Retrieval and validation of global, direct, and diffuse irradiance derived from SEVIRI satellite observations: Surface solar irradiance from satellite. J. Geophys. Res. Atmospheres 118, 2340–2361. https://doi.org/10.1002/jgrd.50194

2012

Roebeling, R.A., Wolters, E.L.A., Meirink, J.F., Leijnse, H., 2012. Triple Collocation of Summer Precipitation Retrievals from SEVIRI over Europe with Gridded Rain Gauge and Weather Radar Data. J. Hydrometeorol. 13, 1552–1566. https://doi.org/10.1175/JHM-D-11-089.1

2011

Mieruch, S., Noël, S., Reuter, M., Bovensmann, H., Burrows, J.P., Schröder, M., Schulz, J., 2011. A New Method for the Comparison of Trend Data with an Application to Water Vapor. J. Clim. 24, 3124–3141. https://doi.org/10.1175/2011JCLI3669.1

Prior 2011

Rinne, J., Aurela, M., Manninen, T., 2009. A Simple Method to Determine the Timing of Snow Melt by Remote Sensing with Application to the CO2 Balances of Northern Mire and Heath Ecosystems. Remote Sens. 1, 1097–1107. https://doi.org/10.3390/rs1041097

 

2020

Bouillon, M., Safieddine, S., Hadji-Lazaro, J., Whitburn, S., Clarisse, L., Doutriaux-Boucher, M., Coppens, D., August, T., Jacquette, E., Clerbaux, C., 2020. Ten-Year Assessment of IASI Radiance and Temperature. Remote Sens. 12, 2393. https://doi.org/10.3390/rs12152393

Gleisner, H., Lauritsen, K.B., Nielsen, J.K., Syndergaard, S., 2020. Evaluation of the 15-year ROM SAF monthly mean GPS radio occultation climate data record. Atmospheric Meas. Tech. 13, 3081–3098. https://doi.org/10.5194/amt-13-3081-2020

Li, Y., Yuan, Y., Wang, X., 2020. Assessments of the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Tropospheric Conditions Using Radiosonde Data. Remote Sens. 12, 2717. https://doi.org/10.3390/rs12172717

Saux Picart, S., Marsouin, A., Legendre, G., Roquet, H., Péré, S., Nano-Ascione, N., Gianelli, T., 2020. A Sea Surface Temperature data record (2004–2012) from Meteosat Second Generation satellites. Remote Sens. Environ. 240, 111687. https://doi.org/10.1016/j.rse.2020.111687

Steiner, A.K., Ladstädter, F., Ao, C.O., Gleisner, H., Ho, S.-P., Hunt, D., Schmidt, T., Foelsche, U., Kirchengast, G., Kuo, Y.-H., Lauritsen, K.B., Mannucci, A.J., Nielsen, J.K., Schreiner, W., Schwärz, M., Sokolovskiy, S., Syndergaard, S., Wickert, J., 2020. Consistency and structural uncertainty of multi-mission GPS radio occultation records. Atmospheric Meas. Tech. 13, 2547–2575. https://doi.org/10.5194/amt-13-2547-2020

Xu, X., Stoffelen, A., 2020. Improved Rain Screening for Ku-Band Wind Scatterometry. IEEE Trans. Geosci. Remote Sens. 58, 2494–2503. https://doi.org/10.1109/TGRS.2019.2951726

Xu, X., Stoffelen, A., Meirink, J.F., 2020. Comparison of Ocean Surface Rain Rates From the Global Precipitation Mission and the Meteosat Second-Generation Satellite for Wind Scatterometer Quality Control. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 2173–2182. https://doi.org/10.1109/JSTARS.2020.2995178

2019

García-Haro, F.J., Camacho, F., Martínez, B., Campos-Taberner, M., Fuster, B., Sánchez-Zapero, J., Gilabert, M.A., 2019. Climate Data Records of Vegetation Variables from Geostationary SEVIRI/MSG Data: Products, Algorithms and Applications. Remote Sens. 11, 2103. https://doi.org/10.3390/rs11182103

Rüthrich, F., John, V.O., Roebeling, R.A., Quast, R., Govaerts, Y., Woolliams, E.R., Schulz, J., 2019. Climate Data Records from Meteosat First Generation Part III: Recalibration and Uncertainty Tracing of the Visible Channel on Meteosat-2–7 Using Reconstructed, Spectrally Changing Response Functions. Remote Sens. 11, 1165. https://doi.org/10.3390/rs11101165

Stöckli, R., Bojanowski, J.S., John, V.O., Duguay-Tetzlaff, A., Bourgeois, Q., Schulz, J., Hollmann, R., 2019. Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications. Remote Sens. 11, 1052. https://doi.org/10.3390/rs11091052

Tabata, T., John, V.O., Roebeling, R.A., Hewison, T., Schulz, J., 2019. Recalibration of over 35 Years of Infrared and Water Vapor Channel Radiances of the JMA Geostationary Satellites. Remote Sens. 11, 1189. https://doi.org/10.3390/rs11101189

Wang, Z., Stoffelen, A., Zhang, B., He, Y., Lin, W., Li, X., 2019. Inconsistencies in scatterometer wind products based on ASCAT and OSCAT-2 collocations. Remote Sens. Environ. 225, 207–216. https://doi.org/10.1016/j.rse.2019.03.005

Zeng, Y., Su, Z., Barmpadimos, I., Perrels, A., Poli, P., Boersma, K.F., Frey, A., Ma, X., de Bruin, K., Goosen, H., John, V.O., Roebeling, R., Schulz, J., Timmermans, W., 2019. Towards a Traceable Climate Service: Assessment of Quality and Usability of Essential Climate Variables. Remote Sens. 11, 1186. https://doi.org/10.3390/rs11101186

2018

Carrer, D., Moparthy, S., Lellouch, G., Ceamanos, X., Pinault, F., Freitas, S., Trigo, I., 2018. Land Surface Albedo Derived on a Ten Daily Basis from Meteosat Second Generation Observations: The NRT and Climate Data Record Collections from the EUMETSAT LSA SAF. Remote Sens. 10, 1262. https://doi.org/10.3390/rs10081262

Nightingale, J., Boersma, K., Muller, J.-P., Compernolle, S., Lambert, J.-C., Blessing, S., Giering, R., Gobron, N., De Smedt, I., Coheur, P., George, M., Schulz, J., Wood, A., 2018. Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications. Remote Sens. 10, 1254. https://doi.org/10.3390/rs10081254

2017

Anderson, C., Figa-Saldana, J., Wilson, J.J.W., Ticconi, F., 2017. Validation and Cross-Validation Methods for ASCAT. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 2232–2239. https://doi.org/10.1109/JSTARS.2016.2639784

Feltz, M.L., Borg, L., Knuteson, R.O., Tobin, D., Revercomb, H., Gambacorta, A., 2017. Assessment of NOAA NUCAPS upper air temperature profiles using COSMIC GPS radio occultation and ARM radiosondes. J. Geophys. Res. Atmospheres 122, 9130–9153. https://doi.org/10.1002/2017JD026504

Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Fokke Meirink, J., Devasthale, A., Hanschmann, T., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N., van Zadelhoff, G.-J., Schlundt, C., Stein, D., Finkensieper, S., Håkansson, N., Hollmann, R., 2017a. CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data. Atmospheric Chem. Phys. 17, 5809–5828. https://doi.org/10.5194/acp-17-5809-2017

Riihelä, A., Key, J.R., Meirink, J.F., Kuipers Munneke, P., Palo, T., Karlsson, K.-G., 2017. An intercomparison and validation of satellite-based surface radiative energy flux estimates over the Arctic: ARCTIC RADIATIVE ENERGY FLUXES. J. Geophys. Res. Atmospheres 122, 4829–4848. https://doi.org/10.1002/2016JD026443

Thorne, P.W., Madonna, F., Schulz, J., Oakley, T., Ingleby, B., Rosoldi, M., Tramutola, E., Arola, A., Buschmann, M., Mikalsen, A.C., Davy, R., Voces, C., Kreher, K., De Maziere, M., Pappalardo, G., 2017. Making better sense of the mosaic of environmental measurement networks: a system-of-systems approach and quantitative assessment. Geosci. Instrum. Methods Data Syst. 6, 453–472. https://doi.org/10.5194/gi-6-453-2017

Urraca, Ruben, Gracia-Amillo, A.M., Koubli, E., Huld, T., Trentmann, J., Riihelä, A., Lindfors, A.V., Palmer, D., Gottschalg, R., Antonanzas-Torres, F., 2017a. Extensive validation of CM SAF surface radiation products over Europe. Remote Sens. Environ. 199, 171–186. https://doi.org/10.1016/j.rse.2017.07.013

Urraca, R., Martinez-de-Pison, E., Sanz-Garcia, A., Antonanzas, J., Antonanzas-Torres, F., 2017b. Estimation methods for global solar radiation: Case study evaluation of five different approaches in central Spain. Renew. Sustain. Energy Rev. 77, 1098–1113. https://doi.org/10.1016/j.rser.2016.11.222

Verhoef, A., Vogelzang, J., Verspeek, J., Stoffelen, A., 2017. Long-Term Scatterometer Wind Climate Data Records. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 2186–2194. https://doi.org/10.1109/JSTARS.2016.2615873

Wang, Z., Stoffelen, A., Fois, F., Verhoef, A., Zhao, C., Lin, M., Chen, G., 2017. SST Dependence of Ku- and C-Band Backscatter Measurements. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 2135–2146. https://doi.org/10.1109/JSTARS.2016.2600749

2016

Boylan, P., Wang, J., Cohn, S.A., Hultberg, T., August, T., 2016. Identification and intercomparison of surface-based inversions over Antarctica from IASI, ERA-Interim, and Concordiasi dropsonde data: Surface-Based Inversions Over Antarctica. J. Geophys. Res. Atmospheres 121, 9089–9104. https://doi.org/10.1002/2015JD024724

Bumke, K., König-Langlo, G., Kinzel, J., Schröder, M., 2016. HOAPS and ERA-Interim precipitation over the sea: validation against shipboard in situ measurements. Atmospheric Meas. Tech. 9, 2409–2423. https://doi.org/10.5194/amt-9-2409-2016

Göttsche, F.-M., Olesen, F.-S., Trigo, I., Bork-Unkelbach, A., Martin, M., 2016. Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Africa. Remote Sens. 8, 410. https://doi.org/10.3390/rs8050410

Roman, J., Knuteson, R., August, T., Hultberg, T., Ackerman, S., Revercomb, H., 2016. A global assessment of NASA AIRS v6 and EUMETSAT IASI v6 precipitable water vapor using ground-based GPS SuomiNet stations: IR PWV ASSESSMENT. J. Geophys. Res. Atmospheres 121, 8925–8948. https://doi.org/10.1002/2016JD024806

Tonboe, R.T., Eastwood, S., Lavergne, T., Sørensen, A.M., Rathmann, N., Dybkjær, G., Pedersen, L.T., Høyer, J.L., Kern, S., 2016. The EUMETSAT sea ice concentration climate data record. The Cryosphere 10, 2275–2290. https://doi.org/10.5194/tc-10-2275-2016

2015

Courcoux, N., Schröder, M., 2015b. The CM SAF ATOVS tropospheric water vapour and temperature data record: overview of methodology and evaluation. Earth Syst. Sci. Data Discuss. 8, 127–171. https://doi.org/10.5194/essdd-8-127-2015

Grossi, M., Valks, P., Loyola, D., Aberle, B., Slijkhuis, S., Wagner, T., Beirle, S., Lang, R., 2015. Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B. Atmospheric Meas. Tech. 8, 1111–1133. https://doi.org/10.5194/amt-8-1111-2015

Lattanzio, A., Fell, F., Bennartz, R., Trigo, I.F., Schulz, J., 2015. Quality assessment and improvement of the EUMETSAT Meteosat Surface Albedo Climate Data Record. Atmospheric Meas. Tech. 8, 4561–4571. https://doi.org/10.5194/amt-8-4561-2015

Riihelä, A., Carlund, T., Trentmann, J., Müller, R., Lindfors, A., 2015. Validation of CM SAF Surface Solar Radiation Datasets over Finland and Sweden. Remote Sens. 7, 6663–6682. https://doi.org/10.3390/rs70606663

Wooster, M.J., Roberts, G., Freeborn, P.H., Xu, W., Govaerts, Y., Beeby, R., He, J., Lattanzio, A., Fisher, D., Mullen, R., 2015. LSA SAF Meteosat FRP products – Part 1: Algorithms, product contents, and analysis. Atmospheric Chem. Phys. 15, 13217–13239. https://doi.org/10.5194/acp-15-13217-2015

Zeng, Y., Su, Z., Calvet, J.-C., Manninen, T., Swinnen, E., Schulz, J., Roebeling, R., Poli, P., Tan, D., Riihelä, A., Tanis, C.-M., Arslan, A.-N., Obregon, A., Kaiser-Weiss, A., John, V.O., Timmermans, W., Timmermans, J., Kaspar, F., Gregow, H., Barbu, A.-L., Fairbairn, D., Gelati, E., Meurey, C., 2015. Analysis of current validation practices in Europe for space-based climate data records of essential climate variables. Int. J. Appl. Earth Obs. Geoinformation 42, 150–161. https://doi.org/10.1016/j.jag.2015.06.006

2014

Chiou, E.W., Bhartia, P.K., McPeters, R.D., Loyola, D.G., Coldewey-Egbers, M., Fioletov, V.E., Van Roozendael, M., Spurr, R., Lerot, C., Frith, S.M., 2014. Comparison of profile total ozone from SBUV (v8.6) with GOME-type and ground-based total ozone for a 16-year period (1996 to 2011). Atmospheric Meas. Tech. 7, 1681–1692. https://doi.org/10.5194/amt-7-1681-2014

Hamann, U., Walther, A., Baum, B., Bennartz, R., Bugliaro, L., Derrien, M., Francis, P.N., Heidinger, A., Joro, S., Kniffka, A., Le Gléau, H., Lockhoff, M., Lutz, H.-J., Meirink, J.F., Minnis, P., Palikonda, R., Roebeling, R., Thoss, A., Platnick, S., Watts, P., Wind, G., 2014. Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms. Atmospheric Meas. Tech. 7, 2839–2867. https://doi.org/10.5194/amt-7-2839-2014

Karlsson, K.-G., Johansson, E., 2013. On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset. Atmospheric Meas. Tech. 6, 1271–1286. https://doi.org/10.5194/amt-6-1271-2013

Mieruch, S., Schröder, M., Noël, S., Schulz, J., 2014. Comparison of decadal global water vapor changes derived from independent satellite time series. J. Geophys. Res. Atmospheres 119, 12,489-12,499. https://doi.org/10.1002/2014JD021588

Stengel, M., Kniffka, A., Meirink, J.F., Lockhoff, M., Tan, J., Hollmann, R., 2014. CLAAS: the CM SAF cloud property data set using SEVIRI. Atmospheric Chem. Phys. 14, 4297–4311. https://doi.org/10.5194/acp-14-4297-2014

2013

Chung, E.-S., Soden, B.J., John, V.O., 2013. Intercalibrating Microwave Satellite Observations for Monitoring Long-Term Variations in Upper- and Midtropospheric Water Vapor*. J. Atmospheric Ocean. Technol. 30, 2303–2319. https://doi.org/10.1175/JTECH-D-13-00001.1

Schröder, M., Jonas, M., Lindau, R., Schulz, J., Fennig, K., 2013. The CM SAF SSM/I-based total column water vapour climate data record: methods and evaluation against re-analyses and satellite. Atmospheric Meas. Tech. 6, 765–775. https://doi.org/10.5194/amt-6-765-2013

2012

Bumke, K., Fennig, K., Strehz, A., Mecking, R., Schröder, M., 2012. HOAPS precipitation validation with ship-borne rain gauge measurements over the Baltic Sea. Tellus Dyn. Meteorol. Oceanogr. 64, 18486. https://doi.org/10.3402/tellusa.v64i0.18486

Jonkheid, B.J., Roebeling, R.A., van Meijgaard, E., 2012. A fast SEVIRI simulator for quantifying retrieval uncertainties in the CM SAF cloud physical property algorithm. Atmospheric Chem. Phys. 12, 10957–10969. https://doi.org/10.5194/acp-12-10957-2012

Loyola, D.G., Coldewey-Egbers, M., 2012. Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records. EURASIP J. Adv. Signal Process. 2012, 91. https://doi.org/10.1186/1687-6180-2012-91

2011

Anderson, C., Figa, J., Bonekamp, H., Wilson, J.J.W., Verspeek, J., Stoffelen, A., Portabella, M., 2011. Validation of Backscatter Measurements from the Advanced Scatterometer on MetOp-A. J. Atmospheric Ocean. Technol. 29, 77–88. https://doi.org/10.1175/JTECH-D-11-00020.1

Bugliaro, L., Zinner, T., Keil, C., Mayer, B., Hollmann, R., Reuter, M., Thomas, W., 2011. Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI. Atmospheric Chem. Phys. 11, 5603–5624. https://doi.org/10.5194/acp-11-5603-2011

Prior 2011

Macke, A., Kalisch, J., Hollmann, R., 2010. Validation of downward surface radiation derived from MSG data by in-situ observations over the Atlantic ocean. Meteorol. Z. 19, 155–167. https://doi.org/10.1127/0941-2948/2010/0433

Reuter, M., Thomas, W., Mieruch, S., Hollmann, R., 2010. A Method for Estimating the Sampling Error Applied to CM-SAF Monthly Mean Cloud Fractional Cover Data Retrieved From MSG SEVIRI. IEEE Trans. Geosci. Remote Sens. 48, 2469–2481. https://doi.org/10.1109/TGRS.2010.2041240

Riihelä, A., Laine, V., Manninen, T., Palo, T., Vihma, T., 2010. Validation of the Climate-SAF surface broadband albedo product: Comparisons with in situ observations over Greenland and the ice-covered Arctic Ocean. Remote Sens. Environ. 114, 2779–2790. https://doi.org/10.1016/j.rse.2010.06.014

van der A, R.J., Allaart, M.A.F., Eskes, H.J., 2010. Multi sensor reanalysis of total ozone. Atmospheric Chem. Phys. 10, 11277–11294. https://doi.org/10.5194/acp-10-11277-2010

Wilson, J.J.W., Anderson, C., Baker, M.A., Bonekamp, H., Saldaña, J.F., Dyer, R.G., Lerch, J.A., Kayal, G., Gelsthorpe, R.V., Brown, M.A., Schied, E., Schutz-Munz, S., Rostan, F., Pritchard, E.W., Wright, N.G., King, D., Onel, Ü., 2010. Radiometric Calibration of the Advanced Wind Scatterometer Radar ASCAT Carried Onboard the METOP-A Satellite. IEEE Trans. Geosci. Remote Sens. 48, 3236–3255. https://doi.org/10.1109/TGRS.2010.2045763

Behr, H.D., Hollmann, R., Müller, R.W., 2009. Surface radiation at sea validation of satellite-derived data with shipboard measurements. Meteorol. Z. 18, 61–74. https://doi.org/10.1127/0941-2948/2009/356

Ineichen, P., Barroso, C.S., Geiger, B., Hollmann, R., Marsouin, A., Mueller, R., 2009. Satellite Application Facilities irradiance products: hourly time step comparison and validation over Europe. Int. J. Remote Sens. 30, 5549–5571. https://doi.org/10.1080/01431160802680560

Loyola, D.G., Coldewey-Egbers, R.M., Dameris, M., Garny, H., Stenke, A., Van Roozendael, M., Lerot, C., Balis, D., Koukouli, M., 2009. Global long-term monitoring of the ozone layer – a prerequisite for predictions. Int. J. Remote Sens. 30, 4295–4318. https://doi.org/10.1080/01431160902825016

Reuter, M., Thomas, W., Albert, P., Lockhoff, M., Weber, R., Karlsson, K.-G., Fischer, J., 2009. The CM-SAF and FUB Cloud Detection Schemes for SEVIRI: Validation with Synoptic Data and Initial Comparison with MODIS and CALIPSO. J. Appl. Meteorol. Climatol. 48, 301–316. https://doi.org/10.1175/2008JAMC1982.1

Roebeling, R.A., Deneke, H.M., Feijt, A.J., 2008. Validation of Cloud Liquid Water Path Retrievals from SEVIRI Using One Year of CloudNET Observations. J. Appl. Meteorol. Climatol. 47, 206–222. https://doi.org/10.1175/2007JAMC1661.1

Wolters, E.L.A., Roebeling, R.A., Feijt, A.J., 2008. Evaluation of Cloud-Phase Retrieval Methods for SEVIRI on Meteosat-8 Using Ground-Based Lidar and Cloud Radar Data. J. Appl. Meteorol. Climatol. 47, 1723–1738. https://doi.org/10.1175/2007JAMC1591.1

 

2020

Buehler, S.A., Prange, M., Mrziglod, J., John, V.O., Burgdorf, M., Lemke, O., 2020. Opportunistic Constant Target Matching—A New Method for Satellite Intercalibration. Earth Space Sci. 7. https://doi.org/10.1029/2019EA000856

English, S., Prigent, C., Johnson, B., Yueh, S., Dinnat, E., Boutin, J., Newman, S., Anguelova, M., Meissner, T., Kazumori, M., Weng, F., Supply, A., Kilic, L., Bettenhausen, M., Stoffelen, A., Accadia, C., 2020. Reference-Quality Emission and Backscatter Modeling for the Ocean. Bull. Am. Meteorol. Soc. 101, E1593–E1601. https://doi.org/10.1175/BAMS-D-20-0085.1

Hewison, T.J., Doelling, D.R., Lukashin, C., Tobin, D., O. John, V., Joro, S., Bojkov, B., 2020. Extending the Global Space-Based Inter-Calibration System (GSICS) to Tie Satellite Radiances to an Absolute Scale. Remote Sens. 12, 1782. https://doi.org/10.3390/rs12111782

Saux Picart, S., Marsouin, A., Legendre, G., Roquet, H., Péré, S., Nano-Ascione, N., Gianelli, T., 2020. A Sea Surface Temperature data record (2004–2012) from Meteosat Second Generation satellites. Remote Sens. Environ. 240, 111687. https://doi.org/10.1016/j.rse.2020.111687

Xu, X., Stoffelen, A., Meirink, J.F., 2020. Comparison of Ocean Surface Rain Rates From the Global Precipitation Mission and the Meteosat Second-Generation Satellite for Wind Scatterometer Quality Control. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 2173–2182. https://doi.org/10.1109/JSTARS.2020.2995178

2019

Bumke, K., Pilch Kedzierski, R., Schröder, M., Klepp, C., Fennig, K., 2019. Validation of HOAPS Rain Retrievals against OceanRAIN In-Situ Measurements over the Atlantic Ocean. Atmosphere 10, 15. https://doi.org/10.3390/atmos10010015

Hans, I., Burgdorf, M., Buehler, S., Prange, M., Lang, T., John, V., 2019. An Uncertainty Quantified Fundamental Climate Data Record for Microwave Humidity Sounders. Remote Sens. 11, 548. https://doi.org/10.3390/rs11050548

John, V.O., Tabata, T., Rüthrich, F., Roebeling, R., Hewison, T., Stöckli, R., Schulz, J., 2019. On the Methods for Recalibrating Geostationary Longwave Channels Using Polar Orbiting Infrared Sounders. Remote Sens. 11, 1171. https://doi.org/10.3390/rs11101171

Lavergne, T., Sørensen, A.M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M.A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., Pedersen, L.T., 2019. Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records. The Cryosphere 13, 49–78. https://doi.org/10.5194/tc-13-49-2019

Quast, R., Giering, R., Govaerts, Y., Rüthrich, F., Roebeling, R., 2019. Climate Data Records from Meteosat First Generation Part II: Retrieval of the In-Flight Visible Spectral Response. Remote Sens. 11, 480. https://doi.org/10.3390/rs11050480

Stöckli, R., Bojanowski, J.S., John, V.O., Duguay-Tetzlaff, A., Bourgeois, Q., Schulz, J., Hollmann, R., 2019. Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications. Remote Sens. 11, 1052. https://doi.org/10.3390/rs11091052

2018

Govaerts, Y., Rüthrich, F., John, V., Quast, R., 2018. Climate Data Records from Meteosat First Generation Part I: Simulation of Accurate Top-of-Atmosphere Spectral Radiance over Pseudo-Invariant Calibration Sites for the Retrieval of the In-Flight Visible Spectral Response. Remote Sens. 10, 1959. https://doi.org/10.3390/rs10121959

Riihelä, A., Kallio, V., Devraj, S., Sharma, A., Lindfors, A., 2018. Validation of the SARAH-E Satellite-Based Surface Solar Radiation Estimates over India. Remote Sens. 10, 392. https://doi.org/10.3390/rs10030392

Su, Z., Timmermans, W., Zeng, Y., Schulz, J., John, V.O., Roebeling, R.A., Poli, P., Tan, D., Kaspar, F., Kaiser-Weiss, A.K., Swinnen, E., Toté, C., Gregow, H., Manninen, T., Riihelä, A., Calvet, J.-C., Ma, Y., Wen, J., 2018. An Overview of European Efforts in Generating Climate Data Records. Bull. Am. Meteorol. Soc. 99, 349–359. https://doi.org/10.1175/BAMS-D-16-0074.1

Wang, Y., Trentmann, J., Yuan, W., Wild, M., 2018. Validation of CM SAF CLARA-A2 and SARAH-E Surface Solar Radiation Datasets over China. Remote Sens. 10, 1977. https://doi.org/10.3390/rs10121977

2017

de Kloe, J., Stoffelen, A., Verhoef, A., 2017. Improved Use of Scatterometer Measurements by Using Stress-Equivalent Reference Winds. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 2340–2347. https://doi.org/10.1109/JSTARS.2017.2685242

Hans, I., Burgdorf, M., John, V.O., Mittaz, J., Buehler, S.A., 2017. Noise performance of microwave humidity sounders over their lifetime. Atmospheric Meas. Tech. 10, 4927–4945. https://doi.org/10.5194/amt-10-4927-2017

Karlsson, K.-G., Håkansson, N., Mittaz, J., Hanschmann, T., Devasthale, A., 2017b. Impact of AVHRR Channel 3b Noise on Climate Data Records: Filtering Method Applied to the CM SAF CLARA-A2 Data Record. Remote Sens. 9, 568. https://doi.org/10.3390/rs9060568

Kobayashi, S., Poli, P., John, V.O., 2017. Characterisation of Special Sensor Microwave Water Vapor Profiler (SSM/T-2) radiances using radiative transfer simulations from global atmospheric reanalyses. Adv. Space Res. 59, 917–935. https://doi.org/10.1016/j.asr.2016.11.017

Koldunov, N.V., Köhl, A., Serra, N., Stammer, D., 2017. Sea ice assimilation into a coupled ocean–sea ice model using its adjoint. The Cryosphere 11, 2265–2281. https://doi.org/10.5194/tc-11-2265-2017

Rivas, M.B., Stoffelen, A., Verspeek, J., Verhoef, A., Neyt, X., Anderson, C., 2017. Cone Metrics: A New Tool for the Intercomparison of Scatterometer Records. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 2195–2204. https://doi.org/10.1109/JSTARS.2017.2647842

Ticconi, F., Anderson, C., Figa-Saldana, J., Wilson, J.J.W., Bauch, H., 2017. Analysis of Radio Frequency Interference in Metop ASCAT Backscatter Measurements. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 2360–2371. https://doi.org/10.1109/JSTARS.2016.2640561

Stoffelen, A., Verspeek, J.A., Vogelzang, J., Verhoef, A., 2017. The CMOD7 Geophysical Model Function for ASCAT and ERS Wind Retrievals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 2123–2134. https://doi.org/10.1109/JSTARS.2017.2681806

Tilstra, L.G., Tuinder, O.N.E., Wang, P., Stammes, P., 2017. Surface reflectivity climatologies from UV to NIR determined from Earth observations by GOME-2 and SCIAMACHY: SURFACE REFLECTIVITY CLIMATOLOGIES. J. Geophys. Res. Atmospheres 122, 4084–4111. https://doi.org/10.1002/2016JD025940

2016

Munro, R., Lang, R., Klaes, D., Poli, G., Retscher, C., Lindstrot, R., Huckle, R., Lacan, A., Grzegorski, M., Holdak, A., Kokhanovsky, A., Livschitz, J., Eisinger, M., 2016. The GOME-2 instrument on the Metop series of satellites: instrument design, calibration, and level 1 data processing – an overview. Atmospheric Meas. Tech. 9, 1279–1301. https://doi.org/10.5194/amt-9-1279-2016

Quast, R., Govaerts, Y., Rüthrich, F., Giering, R., Roebeling, R., 2016. Creating Fidelitous Climate Data Records from Meteosat First Generation Observations 1952152 Bytes. https://doi.org/10.6084/M9.FIGSHARE.3412201.V1

Tonboe, R.T., Eastwood, S., Lavergne, T., Sørensen, A.M., Rathmann, N., Dybkjær, G., Pedersen, L.T., Høyer, J.L., Kern, S., 2016. The EUMETSAT sea ice concentration climate data record. The Cryosphere 10, 2275–2290. https://doi.org/10.5194/tc-10-2275-2016

2015

Courcoux, N., Schröder, M., 2015a. The CM SAF ATOVS data record: overview of methodology and evaluation of total column water and profiles of tropospheric humidity. Earth Syst. Sci. Data 7, 397–414. https://doi.org/10.5194/essd-7-397-2015

Wooster, M.J., Roberts, G., Freeborn, P.H., Xu, W., Govaerts, Y., Beeby, R., He, J., Lattanzio, A., Fisher, D., Mullen, R., 2015. LSA SAF Meteosat FRP products – Part 1: Algorithms, product contents, and analysis. Atmospheric Chem. Phys. 15, 13217–13239. https://doi.org/10.5194/acp-15-13217-2015

2014

Borde, R., Doutriaux-Boucher, M., 2014. Extraction des vecteurs vents à partir d’images satellite. La Météorologie 8, 27. https://doi.org/10.4267/2042/54333

2013

John, V.O., Allan, R.P., Bell, W., Buehler, S.A., Kottayil, A., 2013a. Assessment of intercalibration methods for satellite microwave humidity sounders: INTERCALIBRATION METHODS FOR HUMIDITY SOUNDERS. J. Geophys. Res. Atmospheres 118, 4906–4918. https://doi.org/10.1002/jgrd.50358

John, V.O., Holl, G., Atkinson, N., Buehler, S.A., 2013b. Monitoring scan asymmetry of microwave humidity sounding channels using simultaneous all angle collocations (SAACs): SCAN BIAS OF MICROWAVE HUMIDITY SOUNDERS. J. Geophys. Res. Atmospheres 118, 1536–1545. https://doi.org/10.1002/jgrd.50154

Sanchez-Lorenzo, A., Wild, M., Trentmann, J., 2013. Validation and stability assessment of the monthly mean CM SAF surface solar radiation dataset over Europe against a homogenized surface dataset (1983–2005). Remote Sens. Environ. 134, 355–366. https://doi.org/10.1016/j.rse.2013.03.012

Schröder, M., Jonas, M., Lindau, R., Schulz, J., Fennig, K., 2013. The CM SAF SSM/I-based total column water vapour climate data record: methods and evaluation against re-analyses and satellite. Atmospheric Meas. Tech. 6, 765–775. https://doi.org/10.5194/amt-6-765-2013

2012

August, T., Klaes, D., Schlüssel, P., Hultberg, T., Crapeau, M., Arriaga, A., O’Carroll, A., Coppens, D., Munro, R., Calbet, X., 2012. IASI on Metop-A: Operational Level 2 retrievals after five years in orbit. J. Quant. Spectrosc. Radiat. Transf. 113, 1340–1371. https://doi.org/10.1016/j.jqsrt.2012.02.028

Mueller, R., Behrendt, T., Hammer, A., Kemper, A., 2012. A New Algorithm for the Satellite-Based Retrieval of Solar Surface Irradiance in Spectral Bands. Remote Sens. 4, 622–647. https://doi.org/10.3390/rs4030622

2011

Prior 2011

Andersson, A., Fennig, K., Klepp, C., Bakan, S., Graßl, H., Schulz, J., 2010. The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3. Earth Syst. Sci. Data 2, 215–234. https://doi.org/10.5194/essd-2-215-2010

Loew, A., Govaerts, Y., 2010. Towards Multidecadal Consistent Meteosat Surface Albedo Time Series. Remote Sens. 2, 957–967. https://doi.org/10.3390/rs2040957

Govaerts, Y.M., Lattanzio, A., 2007. Retrieval error estimation of surface albedo derived from geostationary large band satellite observations: Application to Meteosat-2 and Meteosat-7 data. J. Geophys. Res. 112, D05102. https://doi.org/10.1029/2006JD007313

Lattanzio, A., Govaerts, Y.M., Pinty, B., 2007. Consistency of surface anisotropy characterization with meteosat observations. Adv. Space Res. 39, 131–135. https://doi.org/10.1016/j.asr.2006.02.049

Govaerts, Y.M., Pinty, B., Taberner, M., Lattanzio, A., 2006. Spectral Conversion of Surface Albedo Derived From Meteosat First Generation Observations. IEEE Geosci. Remote Sens. Lett. 3, 23–27. https://doi.org/10.1109/LGRS.2005.854202

Roebeling, R.A., Feijt, A.J., Stammes, P., 2006. Cloud property retrievals for climate monitoring: Implications of differences between Spinning Enhanced Visible and Infrared Imager (SEVIRI) on METEOSAT-8 and Advanced Very High Resolution Radiometer (AVHRR) on NOAA-17. J. Geophys. Res. 111, D20210. https://doi.org/10.1029/2005JD006990

Pinty, B., Roveda, F., Verstraete, M.M., Gobron, N., Govaerts, Y., Martonchik, J.V., Diner, D.J., Kahn, R.A., 2000a. Surface albedo retrieval from Meteosat: 1. Theory. J. Geophys. Res. Atmospheres 105, 18099–18112. https://doi.org/10.1029/2000JD900113

Pinty, B., Roveda, F., Verstraete, M.M., Gobron, N., Govaerts, Y., Martonchik, J.V., Diner, D.J., Kahn, R.A., 2000b. Surface albedo retrieval from Meteosat: 2. Applications. J. Geophys. Res. Atmospheres 105, 18113–18134. https://doi.org/10.1029/2000JD900114


(see https://www.zotero.org/roebeling/collections/N2LZM3HI/tags/Application/collection )

(see https://www.zotero.org/roebeling/collections/N2LZM3HI/tags/Validation/collection )

(see https://www.zotero.org/roebeling/collections/N2LZM3HI/tags/Method/collection )

(Belmonte Rivas and Stoffelen, 2019; Docquier et al., 2019; Gignac et al., 2019; Landy et al., 2017b, 2017b; Marseille and Stoffelen, 2017; Massonnet et al., 2018; Stoffelen et al., 2017; Trindade et al., 2020; von Schuckmann et al., 2019; Zampieri et al., 2018)Boylan, P., Wang, J., Cohn, S. A., Hultberg, T., & August, T. (2016). Identification and intercomparison of surface‐based inversions over

Antarctica from IASI, ERA‐Interim, and Concordiasi dropsonde data. Journal of Geophysical Research: Atmospheres, 121, 9089